Mesoscale Explicit Ecogeomorphic Barrier model v1.0
Journal Article: Reeves, I. R. B., Ashton, A. D., Lentz, E. L., Sherwood, C. R., Passeri, D. L., & Zeigler., S. L. (in review). Projecting management-relevant change of undeveloped coastal barriers with the Mesoscale Explicit Ecogeomorphic Barrier model (MEEB) v1.0.
Software Package: Reeves, I. R. B., 2024, Mesoscale Explicit Ecogeomorphic Barrier Model (MEEB) v1.0: U.S. Geological Survey software release.
The Mesoscale Explicit Ecogeomorphic Barrier model (MEEB) resolves cross-shore and alongshore changes in topography and ecology to simulate the ecogeomorphic evolution of an undeveloped barrier or barrier segment. The model is designed to operate over spatiotemporal scales most relevant to coastal management practices (decades and kilometers). MEEB uses weekly timesteps and meter-scale cell size.
MEEB explicitly yet efficiently simulates coupled aeolian, marine, vegetation, and shoreline components of barrier ecogeomorphology, including dune growth, vegetation expansion and mortality, beach and foredune erosion, barrier overwash, and shoreline and shoreface change processes.
MEEB requires Python 3 and the libraries listed in the project's requirements.txt file.
First, download the source code for MEEB into a new project directory.
Then, run the following from the top-level folder (the one that contains setup.py) to install into the current environment:
pip install -e .
Input files are located in the /Input directory and include initial topography and storm timeseries. A set of
commonly-manipulated parameters can be adjusted in the initializarion of the MEEB class in a model run script (see examples below).
Run scripts are separate files that run model simulations and produce model output (i.e., plot results and/or save data). These scripts can be manipulated to alter model parameters and simulation specifications according to the needs of the modeler. Two run scripts for MEEB are available:
/Tools/run_MEEB.py runs a single deterministic simulation
/Tools/run_MEEB_Probabilistic.py produces probabilistic projections that account for uncertainties related to future forcing
conditions and the inherent randomness of natural phenomena
In-depth tutorials for running MEEB deterministically and probabilistically are stored in the /Notebooks folder. These Jupyter
Notebooks step through an example deterministic or probabilistic run script with descriptions and explanations for each block of code.
To run a Notebook, navigate to the main /MEEB directory in a terminal window and type:
jupyter notebook
This will launch an interactive Notebook environment in your default web browser. In the Jupyter Notebook web browser, open on the
/Notebook folder, and double click to open one of two Notebook tutorials:
run_MEEB_Deterministic.ipynb for a simple deterministic run. Start here!
run_MEEB_Probabilistic.ipynb for a much more complex probabilistic projection.
Progress through the tutorials, cell-by-cell, by clicking the play button on the top ribbon. You can also edit cells to explore different parameter values. Click here for more documentation on Jupyter Notebooks.
-
Import MEEB and dependencies:
from meeb import MEEB import matplotlib.pyplot as plt -
Create an instance of the MEEB class, here specifying a 3 yr, 500-m-long simulation with a relative sea-level rise rate of 3 mm/yr:
meeb = MEEB( simulation_time_yr=3, # [yr] RSLR=0.003, # [m/yr] alongshore_domain_boundary_min=500, # [m] alongshore_domain_boundary_max=1000, # [m] ) -
Loop through time with MEEB's
update()function:for time_step in range(int(meeb.iterations)): meeb.update(time_step) -
Once the simulation finishes, plot results such as the elevation at the last time step:
plt.matshow(meeb.topo, cmap='terrain', vmin=-1, vmax=6) plt.show()
Refer to the Notebook tutorials and provided run scripts referenced above for additional complexity.